An adaptive network model covering metacognition to control adaptation for multiple mental models

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning multiplication at the primary school.     

A two-part evaluation approach for measuring the usability and user experience of an Augmented Reality-based assistance system to support the temporal coordination of spatially dispersed teams

One key predictor of team performance and productivity is the teams’ ability to coordinate its subtasks as precisely as possible over time. Thereby, the quality of the temporal coordination in teams is highly dependent on several cognitive team skills, like for instance the ability to build a precise and stable mental model of the situation (shared situation awareness) and of the team task process (task state awareness/ TSA). To support these cognitive skills in teams, various methods and trainings already exist. However, considering teams that work de-located, the prerequisites and therefore the requirements for team supportive methods change. This work presents the results of an empirical usability and user experience (UX) study of an interface for an Augmented Reality-based assistance system for spatially dispersed teams, called Ambient Awareness Tool (AAT). The AAT interface consists of graphical information about the teamwork process and aims at enhancing the TSA of the team, thereby supporting its temporal coordination. Within the framework of a participatory design process we conducted a two-part expert-user-study, comprising a laboratory experiment for the evaluation of the interface usability and a follow-up online survey to additionally investigate the UX. By means of the usability scores we first inferred three interface configuration clusters. A subsequent UX evaluation then allowed us to designate the configuration cluster with the highest usability and UX scores. As a result, we defined one interface configuration that will be investigated concerning its actual impact on the temporal coordination of spatially dispersed teams in a further study.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.     

Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.     

动作视频游戏对发展性阅读障碍者阅读技能的影响及其内在机制

发展性阅读障碍是一种在获得阅读技能方面的特殊困难, 这种障碍会严重影响个体的发展, 如何帮助发展性阅读障碍者改善其阅读技能是近年来研究的焦点。传统的干预方法主要针对发展性阅读障碍者的语音缺陷, 这类方法存在一些问题, 如费时费力、给阅读障碍者带来阅读压力等。近年来, 大部分研究表明通过趣味性的动作视频游戏训练可以显著地提高发展性阅读障碍者的阅读技能, 但是其背后的机制尚不明确。基于大细胞通路缺陷理论框架, 从视觉空间注意、注意跨通道转换、视觉运动加工等方面来梳理动作视频游戏与阅读之间的关系, 揭示了动作视频游戏训练对阅读效率影响的可能内在机制。未来的研究可以在大细胞通路缺陷理论的框架下, 深入分析动作视频游戏改善阅读的神经机制, 并尝试开发更适合发展性阅读障碍者的干预程序。     

贝叶斯决策理论对复杂运动决策中运动预期的启发——以网球和足球为例

对运动决策的研究是认知运动心理学的一个重要领域。运动预期被认为是运动决策的核心, 受到运动学和非运动学信息的影响。其中, 对运动预期研究的一个关键问题是探讨不同信息源对运动结果预期的贡献以及两者之间的相互作用。研究者运用贝叶斯决策理论解释运动预期中不同信息的整合过程, 分析运动员在复杂的竞赛情景中如何做出最佳决策, 尤其是对该理论在网球和足球领域的潜在应用进行分析。在不确定的情况下, 竞技体育中并非所有的选择、结果或概率都是已知的, 故有研究者认为概率论和经典的决策理论不能有效解决此类问题。然而新近提出的启发式近似, 为运动员在贝叶斯框架下如何快速做出选择提供了理论依据：首先, 在复杂和有时间压力的竞赛情景中, 启发式近似假设运动员依据竞赛中不同信息源的不确定程度, 很可能选择在运动学信息和情境先验之间进行切换启发式, 提高运动预期的效率。其次, 判断效用通过卷积效应影响两种信息源的整合, 降低情境先验的影响程度。     

神经美学视角的审美愉悦加工机制

审美对象特有的刺激属性会唤起观赏者特定的情绪或情感反应。个体在欣赏自然、艺术品和其他人类作品时会产生审美愉悦体验。审美愉悦-兴趣模型(PIA)认为, 审美愉悦体验包含审美过程中自动化加工阶段的审美愉悦和控制加工阶段的审美兴趣。近年来, 神经美学研究表明, 负责愉悦和奖赏的眶额叶皮层在审美过程中广泛激活, 是自动化加工阶段初级审美愉悦奖赏的神经基础, 而审美过程中纹状体亚回路中不同的连接和功能作用与两个阶段中审美愉悦的产生都有关联; 上述结果支持了审美愉悦-兴趣模型。但审美高峰体验时默认模式网络(DMN)相关脑区的激活和负责控制与理性思维的外侧前额叶皮层等脑区的失活, 提示在PIA模型强调的自动化加工阶段审美愉悦和控制加工阶段审美兴趣之上, 还有整合升华阶段的审美沉浸愉悦, PIA模型需得到进一步的扩展。未来研究应进一步检验审美愉悦认知加工模型及神经机制, 探索审美对创造力的影响机制和神经基础, 探讨不同审美经验愉悦机制的异同。     

幼儿在联合行动中共同表征能力的发展

为探究幼儿联合行动共同表征能力的发展,实验1通过比较186名3~5岁幼儿在相同任务和不同任务的表现,发现4岁和5岁幼儿在不同任务中的表现比相同任务差;实验2进一步验证幼儿在不同任务中较差的表现源于对自我和同伴的共同表征。结论：3岁幼儿无法在联合任务中同时表征自我和同伴,没有表现出明显的共同表征能力,4岁以上幼儿开始具备稳定的共同表征能力,能够在联合任务中同时表征自己和同伴,由此对自己的行为表现造成干扰。